The Journal of Clinical Pharmacology最新文献

{"title":"Quantitative Systems Pharmacology (QSP): Bridging Biology and Mechanism with Clinical Drug Development Decisions","authors":"Weirong Wang PhD,&nbsp;Alexander V. Ratushny PhD,&nbsp;Steve Chang MS,&nbsp;Yougan Cheng PhD,&nbsp;Jingqi Q. X. Gong PhD,&nbsp;Abhishek Gulati PhD,&nbsp;Emma Hansson PhD,&nbsp;Alexander Kulesza PhD,&nbsp;Federico Reali PhD,&nbsp;Conner Sandefur PhD,&nbsp;Brian J. Schmidt PhD,&nbsp;Fulya Akpinar Singh PhD,&nbsp;Monica E. Susilo PhD,&nbsp;Susana Zaph PhD,&nbsp;Blerta Shtylla PhD","doi":"10.1002/jcph.70289","DOIUrl":"10.1002/jcph.70289","url":null,"abstract":"<div>\u0000 \u0000 <p>Quantitative systems pharmacology (QSP) integrates mechanistic representations of biology with quantitative pharmacology to support decision‑making across the drug development continuum. Over the past two decades, QSP has evolved from an exploratory research activity into an established component of model‑informed drug development (MIDD), particularly in settings where system‑level interactions complicate the interpretation of exposure–response relationships or where direct clinical data are limited. This review outlines the historical development and conceptual foundations of QSP and summarizes representative clinical applications spanning early, mid, and late development. Examples highlight how QSP is used to support mechanism-based dose and regimen selection, optimization of combination strategies, biomarker‑informed patient stratification, and lifecycle management decisions. We also discuss considerations for rigor, credibility, and trustworthiness in alignment with the ICH M15 framework, emphasizing clearly defined questions of interest, context of use, and proportional, decision-driven evaluation of assumptions and uncertainty. Finally, we consider emerging challenges and opportunities for QSP adoption, including reuse of platform models, integration of multi-omics data, and selective incorporation of AI-enabled methods within mechanistically interpretable frameworks.</p>\u0000 </div>","PeriodicalId":22751,"journal":{"name":"The Journal of Clinical Pharmacology","volume":"66 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148971817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Population Pharmacokinetics and Exposure–Response of Sasanlimab, a Checkpoint Inhibitor, in Patients with Non-Muscle Invasive Bladder Cancer and Other Solid Tumors","authors":"Joanna C. Masters PharmD, FCP,&nbsp;Brian Jermain PharmD,&nbsp;Alan Liu PhD,&nbsp;Rossano Cesari PharmD, PhD,&nbsp;Anna Maria Calella PhD,&nbsp;Elisabete Michelon MD,&nbsp;Caimiao Wei PhD,&nbsp;Susana Liu MSCi,&nbsp;Joseph Piscitelli PharmD","doi":"10.1002/jcph.70295","DOIUrl":"10.1002/jcph.70295","url":null,"abstract":"<p>Sasanlimab is a monoclonal antibody (mAb) which blocks human programed cell death protein 1. It is administered subcutaneously (SC), currently under development for non-muscle invasive bladder cancer (NMIBC) with a recommended flat dose of 300 mg every 4 weeks (Q4W). The objectives of the analyses were to characterize the population pharmacokinetics (popPK) of sasanlimab and to determine the exposure–response (ER) relationship between sasanlimab and safety endpoints of interest pooling data across clinical studies including various tumor types. Analyses for efficacy aimed to determine additional ER relationships within the NMIBC population. Sasanlimab PK was typical of IgG4 mAbs and was well characterized by a two-compartment model, with a small volume of distribution (central volume of 3.67 L, peripheral volume of 2.19 L), low systemic clearance (0.142 L/day), and elimination half-life of ∼29 days. Bioavailability from SC dosing was ∼70%. There was no evidence of time-dependent change in sasanlimab clearance, and no meaningful impact of body weight on exposure. There was no association found between sasanlimab exposure and any safety endpoint of interest: treatment-emergent adverse events of Grade ≥2 and Grade ≥3, and immune-related adverse events of Any Grade and Grade ≥3. For efficacy, no association was found between exposure and the key endpoints: investigator-assessed event free survival and complete response (in participants with carcinoma in situ). In total, the popPK results and absence of ER associations for safety and efficacy endpoints support the recommended flat dose of sasanlimab 300 mg SC Q4W.</p>","PeriodicalId":22751,"journal":{"name":"The Journal of Clinical Pharmacology","volume":"66 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13576834/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148971859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nipocalimab Phase 3 Dose Selection for Severe Hemolytic Disease of the Fetus and Newborn","authors":"Jie Zhou PhD,&nbsp;Yuan Xiong PhD,&nbsp;Edwin Lam PharmD,&nbsp;Leona E. Ling PhD,&nbsp;Shumyla Saeed-Khawaja MD,&nbsp;Yosuke Komatsu MD, PhD,&nbsp;An Vermeulen PhD,&nbsp;Mahesh N. Samtani PhD,&nbsp;Jocelyn H. Leu PharmD, PhD","doi":"10.1002/jcph.70259","DOIUrl":"https://doi.org/10.1002/jcph.70259","url":null,"abstract":"<p>Nipocalimab, a neonatal Fc receptor (FcRn) blocker, is under evaluation for severe hemolytic disease of the fetus and newborn (HDFN). In the Phase 2 UNITY trial, weekly intravenous antenatal treatment with nipocalimab at dose regimens of 30 and 45 mg/kg prevented fetal anemia requiring intrauterine transfusion (IUT) in 54% of high-risk pregnancies and delayed the need for IUTs versus their previous pregnancies in the remaining 46% of pregnancies. This analysis aimed to select a weekly dose regimen of nipocalimab for the Phase 3 study in severe HDFN (NCT05912517) that maintains FcRn blockade throughout antenatal treatment, including with an unplanned dosing delay of up to 3 days. Observed pharmacokinetic/pharmacodynamic (PK/PD) data from UNITY (i.e., nipocalimab concentrations, FcRn occupancy, and serum IgG) were analyzed using a model-based approach. A PK/PD model originally developed in nonpregnant participants was updated to incorporate gestational weight gain. Nipocalimab PK and FcRn occupancy were described by a two-compartment model with nonlinear, dose-dependent PK, which captured longitudinal PK, FcRn occupancy, and IgG profiles during dosing and return toward baseline postpartum after discontinuation. Both 30 and 45 mg/kg achieved ∼80%–85% reductions in maternal IgG; however, 30 mg/kg showed greater variability in predose trough concentrations, increasing the risk of falling below concentrations required for full FcRn occupancy across antenatal treatment. Simulations incorporating PK/PD variability indicated that 45 mg/kg weekly per current weight maintained full FcRn occupancy in &gt;95% of pregnant individuals, even with dosing delays up to 3 days. Exploratory exposure–response analyses supported 45 mg/kg for the Phase 3 HDFN study.</p>","PeriodicalId":22751,"journal":{"name":"The Journal of Clinical Pharmacology","volume":"66 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jcph.70259","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148943272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pharmacokinetics and Pharmacodynamics of Antimalarial Agents: Optimizing Combination Therapies to Overcome Resistance Mechanisms","authors":"Kiana Pica MS,&nbsp;Oliver Grundmann MS, PhD, FCP,&nbsp;Francine Johansson Azeredo BPharm, PhD","doi":"10.1002/jcph.70291","DOIUrl":"10.1002/jcph.70291","url":null,"abstract":"<p>Malaria remains a persistent global health challenge, worsened by the emergence of drug-resistant <i>Plasmodium</i> strains. This review synthesizes the pharmacokinetics (PK) and pharmacodynamics (PD) of key antimalarial agents, evaluating how these properties influence the performance of current pharmacological regimens. Findings in the literature indicate that artemisinin-based combination therapies (ACTs) achieve rapid parasite clearance by using short-acting artemisinin derivatives paired with longer-acting partner drugs. PK/PD modeling consistently demonstrates that well-matched half-lives and sustained post-treatment exposure are critical to prevent functional monotherapy-related resistance. Non-artemisinin and triple combination regimens show promise in overcoming multidrug resistance, but gaps remain in exposure–response characterization, dose alignment, and population-specific optimization. A discussion of PK/PD modeling across agents, including chloroquine, artemisinin and its derivatives, mefloquine, primaquine, and tafenoquine, highlights how inadequate drug exposure, mismatched partner drug kinetics, and host metabolic variability contribute to treatment failure. Collectively, the evidence suggests that refining combination regimens through PK/PD-guided dose optimization is crucial for maintaining efficacy and preventing resistance. Future research must prioritize host-specific factors, stage-specific drug activity, and optimized combination regimens to improve therapeutic outcomes and support malaria eradication.</p>","PeriodicalId":22751,"journal":{"name":"The Journal of Clinical Pharmacology","volume":"66 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model-Informed Dose Selection of Intranasal Epinephrine in Young Children","authors":"Manav Jain MD,&nbsp;Priscila A Yamamoto PhD,&nbsp;Amira Soliman PhD,&nbsp;Richard Lowenthal MSc,&nbsp;Valvanera Vozmediano PhD,&nbsp;Stephan Schmidt PhD, FCP,&nbsp;Natalia de Moraes PhD","doi":"10.1002/jcph.70294","DOIUrl":"10.1002/jcph.70294","url":null,"abstract":"<p>Anaphylaxis is a rapid, potentially fatal allergic reaction with a global prevalence of 0.04%-1.8%. Early intramuscular epinephrine administration is recommended. However, fear, pain, cost, or social barriers often delay treatment, leading to poor outcomes. To address these challenges, an intranasal (IN) epinephrine formulation was developed. Pharmacokinetic (PK) data for IN epinephrine are absent in children &lt;4 years (7.5-15 kg) and limited in children ≥4 years. To inform pediatric dosing, we developed a population PK model to support weight-based dosing (≥30 kg, 15-&lt;30 kg, and 7.5-&lt;15 kg) in children. Pooled single-dose IN epinephrine data from six studies (five adults, one pediatric), with 255 subjects and 3564 observations, were analyzed using nonlinear mixed-effects modeling (NONMEM v7.5). Data handling included baseline imputation, exclusion of endogenous concentrations (&gt;4 h), and BLQ management via the M4 method. One- and two-compartment models with allometric scaling by body weight were evaluated. Model performance was assessed using diagnostic plots, objective function value, and prediction-corrected visual predictive checks (pcVPC). Pediatric exposure simulations used CDC-based virtual populations to guide weight-based dosing. A two-compartment model with first-order absorption best described the data, with acceptable relative standard error &lt;30% and no evidence of model misspecification. pcVPC demonstrated good agreement across adult and pediatric subgroups. Simulations indicated that the pediatric doses of 2 mg (≥30 kg) and 1 mg (15-&lt;30 kg) achieved exposure comparable to the adult 2 mg reference, supporting the dosing strategy. Model-informed simulations support weight-based IN epinephrine dosing in young children, addressing a critical gap in anaphylaxis management.</p>","PeriodicalId":22751,"journal":{"name":"The Journal of Clinical Pharmacology","volume":"66 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148913495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Rise of the Digital Health Technology–AI Coupled System in the AI Era of Drug Development","authors":"Hanrui Zhang PhD,&nbsp;Gabriel K. Innes PhD,&nbsp;Jonas Santiago MS,PharmD,&nbsp;Ayoub Suliman PharmD,&nbsp;Norah Xiao MS,&nbsp;Hao Zhu PhD,&nbsp;Dianne Paraoan MS,&nbsp;Jeffery Siegel MD,&nbsp;Yutao Gong PhD,&nbsp;Ruihao Huang PhD,&nbsp;Menglun Wang PhD,&nbsp;Lixia Zhang PhD,&nbsp;Qi Liu PhD","doi":"10.1002/jcph.70287","DOIUrl":"10.1002/jcph.70287","url":null,"abstract":"&lt;p&gt;The modern clinical investigation is undergoing a profound structural evolution. Previously, a comprehensive landscape analysis of regulatory submissions to the US Food and Drug Administration (FDA) during 2016 to 2021 demonstrated a rapid, exponential increase in the application of artificial intelligence and machine learning (AI/ML) across the drug development lifecycle.&lt;span&gt;&lt;sup&gt;1, 2&lt;/sup&gt;&lt;/span&gt; At the same time, an expansion of digital health technologies (DHTs) in drug development is also observed, such as smart watches, phone applications, and other mobile technologies, enabling continuous capture of vital physiological and behavioral data real-time and remotely outside of traditional trial site.&lt;span&gt;&lt;sup&gt;3-5&lt;/sup&gt;&lt;/span&gt; However, these technologies are not advancing in isolation; many DHT applications have the potential to be intrinsically coupled with AI.&lt;/p&gt;&lt;p&gt;For the purposes of this commentary, we explore these coupled technologies as an AI–DHT coupled system. The AI–DHT coupled system describes the synergistic relationship between DHTs and AI in drug development. Within this emerging AI–DHT coupled system, DHT collects large amounts of data from patients remotely outside of traditional clinical trial sites, and AI can provide the necessary computational engine for signal processing, feature selection, and clinical translation. Together, they are driving a steady paradigm shift in drug development, gradually augmenting the traditional, episodic, site-centric model with a continuous, patient-centric model, as reflected in FDA's guidance on conducting clinical trials with decentralized element&lt;span&gt;&lt;sup&gt;6&lt;/sup&gt;&lt;/span&gt; (Figure 1).&lt;/p&gt;&lt;p&gt;To fully appreciate the scope of this evolving relationship, it is essential to trace how the AI–DHT coupled system is embedded into the operational and clinical continuum of drug development. Rather than serving as isolated point solutions, these coupled technologies create a continuous thread of data generation and algorithmic translation in all stages of clinical trials. In reviewing recent regulatory submissions, the use of this AI–DHT coupled system can be mapped across four distinct chronological phases: optimizing initial trial enrollment, engagement, and protocol adherence; establishing baseline digital phenotypes for trial enrichment; conducting active safety surveillance; and developing novel digitally derived endpoints and biomarkers.&lt;/p&gt;&lt;p&gt;The initial impact of the AI–DHT coupled system is observed directly in promoting trial enrollment and expanding patient access to clinical trials. AI–DHT coupled systems can help patient enrollment in trials through automation of enrollment procedures or enabling timely enrollment of patients with acute and transient conditions, for example, patients with sepsis in emergency department, where the onset could take from a few hours to a few days. By leveraging the remote data collection capabilities established by DHTs, investigators can also enroll hist","PeriodicalId":22751,"journal":{"name":"The Journal of Clinical Pharmacology","volume":"66 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jcph.70287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148913482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Portability of Surrogate Endpoints","authors":"Henry Bair MD","doi":"10.1002/jcph.70286","DOIUrl":"10.1002/jcph.70286","url":null,"abstract":"&lt;p&gt;In February 2008, the US Food and Drug Administration (FDA) granted accelerated approval to bevacizumab plus paclitaxel for HER2-negative metastatic breast cancer. In the E2100 trial, median progression-free survival was 11.8 months with bevacizumab plus paclitaxel and 5.9 months with paclitaxel alone; overall survival did not differ significantly.&lt;span&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt; Progression-free survival was accepted as reasonably likely to predict clinical benefit. The postapproval AVADO and RIBBON-1 trials showed smaller progression-free-survival gains with other chemotherapy backbones and no statistically significant overall survival benefit.&lt;span&gt;&lt;sup&gt;2, 3&lt;/sup&gt;&lt;/span&gt; In November 2011, the FDA withdrew the breast cancer indication, concluding that the demonstrated benefit did not justify the risks in this population.&lt;span&gt;&lt;sup&gt;4&lt;/sup&gt;&lt;/span&gt; Bevacizumab had, by contrast, improved overall survival in metastatic colorectal cancer.&lt;span&gt;&lt;sup&gt;5&lt;/sup&gt;&lt;/span&gt; The comparison does not by itself validate progression-free survival in colorectal cancer; it illustrates that the clinical meaning of tumor control can differ across diseases.&lt;/p&gt;&lt;p&gt;A surrogate endpoint substitutes an observable measure, such as an interval of tumor control or a laboratory value, used in place of a clinical outcome that matters to patients. Its use rests on the inference that an intervention's effect on the surrogate predicts its effect on the clinical outcome. The bevacizumab case shows that the inference from a progression-free-survival effect to a favorable patient-relevant benefit–risk profile depends on disease and treatment context.&lt;/p&gt;&lt;p&gt;Recent systematic reviews in nononcologic chronic disease and oncology raise concerns about the strength and consistency of this inference.&lt;span&gt;&lt;sup&gt;6, 7&lt;/sup&gt;&lt;/span&gt; Most surrogate markers used to support FDA approvals across nononcologic chronic diseases lack high-strength evidence of association with clinical outcomes in the published meta-analysis literature,&lt;span&gt;&lt;sup&gt;6&lt;/sup&gt;&lt;/span&gt; while treatment-level relationships between progression-free survival and overall survival vary across immune checkpoint inhibitor settings.&lt;span&gt;&lt;sup&gt;7&lt;/sup&gt;&lt;/span&gt; Better validation is necessary, but validation alone cannot establish whether evidence from one context carries into another.&lt;/p&gt;&lt;p&gt;Surrogate endpoints are best understood as inferential bridges. A bridge of this kind holds within specified conditions and does not automatically hold outside them. Here, context means the conditions under which a particular treatment effect on a surrogate is taken to predict a specified clinical benefit: the disease and disease stage; patient population, including age; intervention mechanism or class; comparator, background therapy, and subsequent treatment; the surrogate definition and timing; and the clinical outcome being predicted. A context change is material when a difference could plausibly alter the surrogate–clinical outcome relationship or introduce","PeriodicalId":22751,"journal":{"name":"The Journal of Clinical Pharmacology","volume":"66 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13543047/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148892677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Volumetric Microsampling for Patient-Centric Therapeutic Drug Monitoring in Clinical Pharmacology: A Scoping Review","authors":"Hari Prabhath Tummala PharmD, MSc,&nbsp;Abbie D. Leino PharmD, PhD,&nbsp;Nathaniel J. Rhodes PharmD, MSc,&nbsp;Kevin J. Downes MD,&nbsp;Marc H. Scheetz PharmD, MSc,&nbsp;Manjunath P. Pai PharmD, FCP","doi":"10.1002/jcph.70290","DOIUrl":"10.1002/jcph.70290","url":null,"abstract":"<p>Accurate drug concentration measurement is essential for precision pharmacotherapy, but conventional therapeutic drug monitoring (TDM) requires venous sampling, increasing patient burden, and potentially limiting participation in TDM and model-informed precision dosing (MIPD). Volumetric microsampling enables self-collection of small, fixed volume capillary samples for centralized analysis and may reduce hematocrit-related volumetric bias associated with conventional dried blood spots. This scoping review, conducted according to the PRISMA Extension for Scoping Reviews, synthesized 67 patient-cohort studies published from 2014 through 2026. Studies spanned at least 18 countries and multiple clinical specialties. Liquid chromatography-tandem mass spectrometry was used in 61 studies (91%), and most reported validation using or referencing EMA, FDA, IATDMCT, or CLSI criteria. Recovery was minimally affected across evaluated hematocrit ranges for most analytes, and many analytes remained stable in dried samples, although stability varied by analyte and storage conditions. Where assessed, patient preference was high (80%-100%). However, interpretation against established plasma therapeutic ranges often required whole-blood-to-plasma or capillary-to-venous conversion, with inconsistent derivation and validation methods. Fixed conversion factors performed well for lacosamide and levetiracetam but were less reliable for lamotrigine at high concentrations. Several analyte-matrix combinations, including cefepime, vancomycin, meropenem, paclitaxel, abiraterone, and mitotane, did not consistently meet agreement criteria. Volumetric microsampling can support patient-centered drug monitoring when validated for the specific analyte, matrix, device, and clinical application. Future research should prioritize drug-specific optimization, multicenter validation, standardized conversion reporting, and integration with MIPD platforms.</p>","PeriodicalId":22751,"journal":{"name":"The Journal of Clinical Pharmacology","volume":"66 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13543046/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148892682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Population pharmacokinetics for the CTLA-4 modulator gotistobart in patients with advanced solid tumors","authors":"Tianhong Li MD, PhD,&nbsp;Jiahui Xue MSc,&nbsp;Yang Liu PhD,&nbsp;Jun Yang PhD,&nbsp;Ido D. Weiss PhD,&nbsp;Hasan Abu Kharma BS,&nbsp;Juanjuan Su PhD,&nbsp;Yuye Shen MSc,&nbsp;Tairan Yang PharmD,&nbsp;Kumpal Madrasi PhD,&nbsp;Frank Engler PhD,&nbsp;Shuang Liang PhD,&nbsp;Pan Zheng MD, PhD","doi":"10.1002/jcph.70275","DOIUrl":"10.1002/jcph.70275","url":null,"abstract":"<p>Gotistobart is an investigational, next-generation, humanized IgG1 monoclonal antibody targeting cytotoxic T-lymphocyte antigen-4. It is engineered to preserve cytotoxic T-lymphocyte antigen-4 through endosomal recycling while enhancing intratumoral regulatory T-cell depletion. A population pharmacokinetic model was developed to characterize gotistobart pharmacokinetics and identify covariates contributing to inter-individual variability in patients with advanced solid tumors. Serum concentration-time data were obtained from 632 patients enrolled in three phase I–III clinical trials. In total, 3406 pharmacokinetic observations across a dose range from 0.1 to 10 mg/kg were collected. A two-compartment model with linear elimination best described the data, with inter-individual variability on clearance, central volume, and peripheral volume. Parameter estimates were precise, with typical values of 0.005 L/h for clearance, 0.021 L/h for inter-compartmental clearance, 2.59 L for central volume, and 3.43 L for peripheral volume. Covariates identified in the model included body weight and albumin on clearance and volumes, and sex on volumes, with limited clinically relevant impact. The model was evaluated using parameter precision, goodness-of-fit diagnostics, and prediction-corrected visual predictive checks. The model was used to simulate concentration-time profiles for clinically relevant dosing regimens. Simulations demonstrated the expected steady-state pharmacokinetic behavior, with dose-dependent increases in peak and trough concentrations. Compared with 6 mg/kg every 3 weeks, administration of two 10 mg/kg loading doses followed by 6 mg/kg every 3 weeks accelerated the attainment of steady state. The validated population pharmacokinetic model provides a quantitative foundation for subsequent exposure-response analyses and regimen selection.</p>","PeriodicalId":22751,"journal":{"name":"The Journal of Clinical Pharmacology","volume":"66 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148882341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"One Model to Dose Them All: A Population Pharmacokinetic Model for Multiple Aminoglycosides Across the Human Lifespan","authors":"Dominic M. H. Tong PhD,&nbsp;Jonathan D. Faldasz PharmD, BCPS,&nbsp;Ron J. Keizer PhD, PharmD,&nbsp;Jasmine H. Hughes PhD","doi":"10.1002/jcph.70231","DOIUrl":"10.1002/jcph.70231","url":null,"abstract":"<p>Aminoglycosides require therapeutic drug monitoring due to substantial pharmacokinetic variability across individuals. While population pharmacokinetic (popPK) models have been developed for individual drugs across age groups, and cross-drug maturation models have been explored, it is unclear whether pooling data across aminoglycosides improves predictive performance. We developed and evaluated a unified multi-aminoglycoside popPK model spanning neonates, children, and adults. De-identified data from 5659 amikacin, gentamicin, and tobramycin patients at 154 US institutions were split into training and testing datasets. Predictive performance in the test dataset was evaluated using mean percent error, normalized root mean square error (nRMSE), and clinical accuracy (trough concentrations correctly classified as below or above 1 mg/L and non-troughs within 20% of observation), and compared with selected published popPK models. To assess generalizability, the final model was refit using single-drug datasets and leave-one-subgroup-out datasets. The unified model achieved the lowest nRMSE across age groups, with absolute reductions of 0.6%-24.8%. Clinical accuracy was highest for the unified model, with <i>a posteriori</i> accuracy in pediatric patients of 56.4% versus 48% in the best published model. Leave-one-out refits yielded similar estimates but reduced precision, while single-drug models demonstrated substantially reduced precision, and one such model was not identifiable. This unified model improved predictive performance across drugs and age groups and demonstrated robustness to exclusion of individual drug-age group combinations. These findings support use of a unified aminoglycoside model for Bayesian model-informed precision dosing and demonstrate the advantages of pooling drugs within a class and across developmental stages.</p>","PeriodicalId":22751,"journal":{"name":"The Journal of Clinical Pharmacology","volume":"66 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13539281/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148882280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}